Web usage mining refers to the application of data mining techniques to automatically discover user access patterns from web usage data. Web usage mining typically involves tracking browsing activities using a variety of measures such as individual mouse clicks and time spent on a portion of a webpage in order to have a user's browsing footprint available at the web server. Generally, website mining results include features such as number of page views, number of unique users, browser and operating system information, users website view pattern, widely viewed pages, browser and operating system information.
Website usage mining is useful to track the website usage information in order to track the impact of website and enhance business opportunities. Tracking usage patterns can be useful for identifying which content on the website is being heavily used (and therefore should be kept) and which content is not being heavily used (and may be a candidate for archiving). In addition it helps to improve the website designing by rearranging the content on the pages so that the end users have a more convenient experience while exploring the website. In addition site usage statistics can also keep track of how much storage space the websites are taking, which content on the website is important, and the level of activity for particular websites.
Conventional web usage analysis or web mining typically focus on number of page views for individual pages on a website which does not provide the context of the page views nor how users navigate through the website. Conventional methods also determine individual user sessions however due to large number of distinct user sessions it is difficult to capture, analyze and summarize the user behavior. Thus, the sequence of page views or pattern of website surfing in one or more user sessions or the relationship between users sessions cannot be established, which is useful to analyze and identify the most important data on websites.